Analysing data has become necessary for all departments across the corporate landscape, and as a result, the demand for technical solutions has increased. So, the call for the democratisation of data is also getting louder. Data democratisation is the idea that every individual has access to data, without obstacles or hidden costs, it forms part of the new GDPR regulations and is essential if companies are going to fulfil data access requests.
Achieving this goal, however, is harder than it might appear.
The first issue is that the collection and analysis of data, despite being accessible, can be very time consuming. Because of this, companies tend to outsource the process to other vendors, and the costs involved can be high, especially as these vendors are competing with even bigger corporations like IBM. This is a problem because the essence of data democratisation is freedom, and these expensive solutions do not encourage that.
Once the data has been collected, the next step in the process is to make sure that it is as easy as possible to get access to it. You can have all the data in the world, but until it is accessible, it hasn’t been democratised. The lack of speed in terms of getting information and insight from the raw data can be a major stumbling point when it comes to full democratisation.
These problems have led to many companies adopting a more do-it-yourself approach. By using step by step processes, rather than the all-encompassing service offered by traditional vendors, money is saved. However, the issue here is that to do that, significant levels of skill are required, which immediately excludes people without them from benefiting from the data. Thus, it isn’t fully democratised. One of the potential ways to solve this is to use intelligent AI. Provided they remain affordable, then DIY services, incorporating this technology, could be the way to achieve the goal of total data democratisation.